Exploring Patterns of Generative AI Utilization in Education
DOI:
https://doi.org/10.52731/liir.v004.134Keywords:
generative AI, exercise generation, personalized learning, streamlined educationAbstract
Generative AI, particularly ChatGPT, has gained widespread recognition and is making a significant impact in education. By automating a considerable portion of report assignments and homework, Generative AI, GAI for short, has revolutionized the learning process. Methods and tools should be developed to effectively harness the potential of GAI. The possibilities offered by GAI are extensive, surpassing our current understanding. It enables adaptable education that can cater to the diverse needs of individual students, while also alleviating the workload of teachers, among other benefits. The main objective of this paper is to provide a comprehensive overview of the potential applications of GAI. We concentrate on the shared abstract characteristics of different utilization methods, showcasing their capacity to be classified into discernible patterns. With these patterns, we anticipate the development of future methodologies for the use of GAI in education area.
References
OpenAI, "Educator Considerations for ChatGPT", 2023, [Online] Available: https://platform.openai.com/docs/chatgpt-education.
E. Mollick, "All my classes suddenly became AI classes", 2023, [Online], Available: https://www.oneusefulthing.org/p/all-my-classes-suddenly-became-ai.
J. Rudolph, S. Tan, and T. Tan, “ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?”, J. Appl. Learning & Teaching, vol.6, no.1, 2023, doi: https://doi.org/10.37074/jalt.2023.6.1.9.
X. Zhai,"ChatGPT for Next Generation Science Learning", XRDS:Crossroads, The ACM Magazine for Students, vol. 29, no.3, 2023.
X. Zhai, "ChatGPT user experience: Implications for education", 2023, [Online], Available: https://www.researchgate.net/publication/366463233_ChatGPT_User_Experience_
Implications_for_Education
E. Kasneci, K. Sessler et al. "ChatGPT for good? On Opportunities and Challenges of Large Language Models for Education", Learning and Individual Differences, vol.103, Elsevier, 2023, doi: https://doi.org/10.1016/j.lindif.2023.102274.
C. Kychan and W. Zhou,”Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument”, 2023, [Online], Available: https://doi.org/10.48550/arXiv.2305.01186.
B. McMurtrie, “Teaching: Will ChatGPT change the way you teach?”, The Chronicle of Higher Education, 2023, [Online], Available: https://www.chronicle.com/newsletter/teaching/2023-01-05.
A.Mills, “AI text generators: Sources to stimulate discussion among teachers”, 2023, [Online], Availa-ble:https://docs.google.com/document/d/1V1drRG1XlWTBrEwgGqd-cCySUB12JrcoamB5i16-Ezw/edit#heading=h.qljyuxlccr6.
M. R. Fakhrusy and Y. Widyani, "Moodle Plugins for Quiz Generation using Ge-netic Algorithm", International Conference on Data and Software Engineering (ICoDSE), 2017.
C. Jouault, K. Seta, and Y. Hayasi, “Content-Dependent Question Generation Using LOD for History Learning in Open Learning Space”,New Generation Computing, vol.34, no.4, pp.367-393, 2016.
J. Hays and S. Flower, “Identifying the Organization of Writing Processes”,in L.W.Gregg and E.R. Steinberg (Eds.) Cognitive Process in Writing, Lawrence Earlbaum Associates, 1980.
M. Goto, T.Tanaka, and K. Matsumoto, “Estimating Attention Level from Blinks and Head Movement”, ISCA 30th International Conference on Software Engi-neering and Data Engineeringin (SEDE), 2021.
T. Tanaka, M. Ueda, H. Kasuga, and K. Matsumoto, “Programming Exercise System to Ascertain Students’ Coding Status”, 11th International Conference on Information and Education Technology (ICIET), 2023.